We implement our method based on MMDetection [1]} and MMFewShot [2]}.
We employ the Faster R-CNN [3]} with Feature Pyramid Network [4]} and ResNet-101 [5]} as base model.
All models are trained on 8 Titan-XP GPUs with batch-size 16 (2 images per GPU), and optimized by a standard SGD optimizer with learning rate 0.02, momentum 0.9 and weight decay \(10e^{-4}\) .
We strictly follow the protocol introduced by TFA [6]} without any modifications to initialize the \(^s\) .
MoCo v2 [7]} w/ ResNet-50 [5]} is employed to co-mine novel instances with FSOD model, and we take the C4 feature, , the feature of the last layer of ResNet to compute the cosine similarity. \(\alpha \)  in Equ. 5 is set to be 1.5 for all experiments, and we limit the maximum of novel instances to be 300 and 3000 for PASCAL VOC and MS COCO, respectively.
For the online learning stage, we follow unbiased teacher [9]} to apply weak and strong augmentations to the teacher and student model, respectively.
For PASCAL VOC all models are trained for 18k iterations and decayed at 12k and 16k, respectively, and the confidence threshold \(\delta \)  is set to be 0.7.
For MS COCO all models are trained for 160k iterations and decayed at 110k and 145k, respectively, and the confidence threshold \(\delta \)  is set to be 0.8.
In the last fine-tuning stage, for PASCAL VOC, we only fine-tune the box classifier, predictor and IoU predictor for 4k, 8k, 8k, 8k, 12k iterations for \(K=1, 2, 3, 5, 10\) , respectively.
For MS COCO, we fine-tune the whole R-CNN head for 4k, 8k iterations for \(K=10, 30\) , respectively. The learning rate is set as 0.001 for both datasets.
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